1. Field of the Invention
The field of the invention relates to quantitative optical spectroscopic imaging, and more particularly, to a method of obtaining functional and structural information from multispectral images of in situ analyzed multi-layered, optically dense samples.
2. Background Information
Reflectance Spectroscopy
In general, reflectance spectroscopy is concerned with the identification of a chemical structure comprising a sample through the use of reflected information. Diffuse reflectance spectroscopy is also generally known, and is widely used in the visible to infrared regions of the light spectrum to study materials to include, but not limited to, grains and other food products.
In broad terms, diffuse reflectance spectroscopy utilizes the fact that the sample materials will tend to scatter light in a more or less random fashion. A fraction of the light will eventually be scattered back from the sample and collected by a detector to provide a quantitative or qualitative representation of the sample.
In infrared spectroscopy it is often desirable to use the mid-infrared region of the spectrum. The fundamental vibrational absorptions are strongest here, in the infrared region. The goal of infrared spectroscopy sampling is often to prepare a sample so that it may be analyzed within this mid-infrared light. Reflectance spectroscopy is one very popular way of making a sample compatible with mid-infrared light. If a sample is too thick to get any light through in transmission, often a result can be obtained by reflectance. Reflectance spectroscopy is complicated, however, by the fact that there is more than one optical phenomenon occurring in this mode.
Reflectance of light from a sample can be largely divided into two categories, diffuse reflectance and specular reflectance. The specular reflectance of a sample is the light which does not propagate into the sample, but rather reflects “like a mirror” from the front surface of the sample. This component contains information about the sample at the surface. While the specular component does not physically appear much like an absorbance spectrum, it can be related to the absorbance spectrum of the bulk material through a transformation called the Kramers-Kronig transformation. Still most experts agree that the diffuse component is much more useful for sample qualification and quantification than the specular component. There has been much effort to enhance the diffuse component, to deemphasize the specular component and to essentially cause the reflectance spectrum to be more transmission-like.
Generally these efforts fall largely into three categories: optical discrimination against specular, mechanical discrimination, and secondary methods of sample preparation designed to minimize specular. A fourth non-independent approach is to move away from the mid-infrared region in order to relax the sample preparation requirements. By moving to the near-infrared or visible region of the spectrum, the vibrational spectroscopy becomes more blunt and imprecise, but often this can be made up for by the improvements observed in the quality and signal-to-noise ratio of the data obtained because of improved sampling ability, more appropriate path length, and better discrimination against specular reflectance. This approach is especially useful when quantitative information is desired.
Most experts would agree that the diffuse component is desirable, and even essential, if the sample is thick, layered or non-homogenous.
Light spectroscopic methods are critical to advances in molecular characterization of disease processes. However, most of these methods have been limited to in-vitro or cell culture studies. In fact, strong scattering in almost all tissue types causes dispersion of the photons paths. Hence, quantitative analysis of spectral data obtained from structures below the tissue surface requires accounting for scattering which is a function of wavelength and affects both the penetration of the photons and the pathlengths over which the photons will be subject to molecularly specific absorption.
The goal of much current research is to non-invasively obtain diagnostically useful molecular information from embedded sites. The use of spectroscopic techniques to aid in the diagnosis of various diseases has been limited due to complications arising from the scattering of light in tissue. Traditional spectroscopy relies upon geometrical optics to keep the relative paths of the photons consistent and thus the absorptive effect of analytes may be obtained directly using Beer's law. The scattering of light causes dispersion in the length of the path taken by photons traveling through the tissue. Thus, some photons will travel shorter direct paths and some will travel longer paths. Further, the degree of scatter-induced dispersion is often a function of wavelength.
To reduce the scatter-induced dispersion, many researchers employ devices to limit detected photons to those with more uniform path lengths. This is traditionally accomplished using special optics such as confocal lenses and pinholes. Such methods. are generally limited to depths less than 0.1 mm which is approximately the thickness of the epidermis. More recently, researchers are fixing the source and detector separation by using fiber probes, or special optics and either point illumination with one or more detectors at a fixed distance, or point detection with one or more sources at a fixed distance. By using random or probabilistic methods, information from sites below the epidermis may be obtained. These probes, however, often require direct contact with the patient's skin. Generating an image with such a system typically requires a scanning system which successively probes points on the tissue surface and combines them into an image. This results in long image generation times and the potential for patient motion to create artifacts.
Other researchers employ infrared sensitive multispectral imagers. In the infrared, penetration into the tissue is typically less than 100 microns. The shallow penetration greatly limits potential applications since many anomalous sites are deeper. Due to low absorption, visible and near infrared wavelengths (NIR) light penetrates much deeper with NIR capable of passing through 10 cm or more. What is needed is a non-contact method that uses broad area illumination with multispectral imaging that can provide quantified data on analyte concentrations at points below the epidermis in vivo.
For light to reach an anomalous region in the body it must usually pass through the epidermis. Jacques has shown that the absorption of light in the epidermis is a function of the melanin content in the epidermis. The absorption spectrum for melanin μa(mel)(λ) is shown in FIG. 1 (in units of mm−1 which will be used throughout for absorption and scattering coefficients) [2].
Saidi [3] has published absorption data for de-melaninized skin. All other skin analytes are combined into this single absorption variable μa(skin)(λ) which is a function of wavelength as shown in FIG. 2. The dominant chromophores in this absorption spectrum are lipids and water.
Now that the absorption spectra of the main analytes in the epidermis have been defined, Jacques defines the total absorption of the epidermis as:μa(epi)(λ)=Vmelμa(mel)(λ)+(1−Vmel)μa(skin)(λ)  (1)where Vmel is the volume fraction of melanin which offsets the amount of de-melaninized skin and μa(mel)(λ) is the absorption spectra of melanin.
Jacques and others have shown that the absorption of blood is a function of the volume fraction of oxy-and deoxy-hemoglobin, Voxy and 1−Voxy respectively. This blood volume is confined to the sub-epidermal dermis. The relative absorption spectra for each type of hemoglobin μa(oxy)(λ) and μa(deoxy)(λ) as published by Wray [4] are shown in FIG. 3. The total blood absorption μa(blood)(λ) may be expressed as a volume-based combination of the two:μa(blood)(λ)=Voxyμa(oxy)(λ)+(1−Voxy)μa(deoxy)(λ)  (2)
The total absorption of the dermis from Jacques is therefore:μa(derm)(λ)=Vbloodμa(blood)(λ)+(1−Vblood)μa(skin)(λ)  (3)
Random Walk theory has been used to model the motion of light in high scattering, low absorption media such as tissue. The diffuse reflectance from a collimated point source of illumination has been shown by Gandjbakhche [1]:
                                                        R              =                            ⁢                                                ⅇ                                      -                                                                  24                        ⁢                                                                                                  ⁢                                                                              μ                                                          a                              ⁡                                                              (                                layer                                )                                                                                                              /                                                      μ                                                          a                              ⁡                                                              (                                layer                                )                                                                                      ′                                                                                                                                                                                    24                    ⁢                                                                                  ⁢                                                                  μ                                                  a                          ⁡                                                      (                            layer                            )                                                                                              /                                              μ                                                  s                          ⁡                                                      (                            layer                            )                                                                          ′                                                                                                                                                                                  ⁢                              [                                  1                  -                                      ⅇ                                          -                                                                        24                          ⁢                                                                                    μ                                                              a                                ⁡                                                                  (                                  layer                                  )                                                                                                                      /                                                          μ                                                              a                                ⁡                                                                  (                                  layer                                  )                                                                                            ′                                                                                                                                                            -                                                                                                                      ⁢                              2                ⁢                                                      1                    -                                          cosh                      ⁡                                              [                                                                              24                            ⁢                                                                                                                  ⁢                                                                                          μ                                                                  a                                  ⁡                                                                      (                                    layer                                    )                                                                                                                              /                                                              μ                                                                  a                                  ⁡                                                                      (                                    layer                                    )                                                                                                  ′                                                                                                                                    ]                                                                                                  1                    -                                          ⅇ                                              L                        ⁢                                                                              24                            ⁢                                                                                                                  ⁢                                                                                          μ                                                                  a                                  ⁡                                                                      (                                    layer                                    )                                                                                                                              /                                                              μ                                                                  a                                  ⁡                                                                      (                                    layer                                    )                                                                                                  ′                                                                                                                                                                                                                    ]                                                          (        4        )            where μ′s(λ) is the transport-corrected scattering coefficient which is a function of wavelength and L is a non-dimensional measure of the thickness of the tissue layer. Jacques has expressed the wavelength dependence is a linear combination of the Raleigh, 2×104λ−2.5[mm−1], and Mie, 2×1021λ−4[mm−1], scattering components:μ′s(λ)≈2×104λ−2.5+2×1021λ−4  (5)
It is the wavelength dependence of μ′s(λ), which is shown in FIG. 4, that makes tissue spectroscopy difficult. The effect of changes in μ′s(λ) are tightly linked to changes in the μa(λ) components as may be seen in Eq. 4.
Use of such equations can make possible the ability to obtain diagnostically useful molecular information from embedded sites in a non-invasive manner.
In contrast to the present invention, other studies using various imaging technologies are not capable of accessing tissue utilization of specific informative analytes, are limited to only indirect assessment of tissue status, are labor intensive and/or time-consuming, are susceptible to extrinsic variances such as room or patient temperature, are limited in their utility to only highly vascularized tissue, and are cost prohibitive and/or lack portability (See, e.g., U.S. application Ser. No. 2003/0139667).
Calibration of an imaging system is a crucial but under-emphasized aspect of system performance. The calibration data is a set of weights that normalize system sensitivity to some arbitrary normal. The system sensitivity is a combination of the amount of energy in a particular filter band that is emitted by the light source with the integrated sensitivity of the detector to that energy. Each of those components is generally not known, the net response is traditionally estimated by imaging a calibrated or known reflectance standard.
This type of calibration must be repeated often since the spectral characteristics of the source lamp may change with environment and lamp age. Yet, because of its time-consumption and inconvenience, calibration may not be performed as frequently as needed to assure good results.
Changes in light source spectral characteristics (intensity as a function of wavelength) during a given session can even reduce the effectiveness of calibration methods performed during each imaging session.
What is needed is a method for calibration that can be used without needing pre-or post-data collection hardware calibration information.
Extending Dynamic Range of Imaging Sensors
Traditionally, scientific applications of multispectral image processing has employed monochrome charge coupled devices (CCD). Complementary Metal-Oxide Semiconductor or CMOS detectors, offers an inexpensive alternative to CCDs. The cost of CMOS sensors is rapidly dropping due to high production volumes. In addition to still and video cameras, CMOS imagining sensors are being embedded in computers, personal digital assistants (PDAs) and even cell phones (40 million cameras in cell phones and 30 million still cameras sold 2003). CCD sensors have been used for scientific work due to their characteristic high dynamic range (12 to 16 bits) and low noise for long exposure. Production volumes of CCD chips are relatively stagnant due to the requirement for custom fabrication facilities which keeps CCD chip costs high. CMOS sensors have the benefit of being manufactured on the dynamically improving and expanding CMOS fabrication facility base. While the performance of CMOS sensors has been improving dramatically, CMOS sensors typically have less dynamic range than CCD devices due to the less demanding requirements of consumer products (typically 8 to 12 bits of dynamic range). However, CMOS devices have the large benefit of being able to create sophisticated smart sensors.
Compared to scientific grade CCD cameras, most CMOS sensors are designed for color imaging. The traditional approach for implementing color in both CCD and CMOS devices is to place a pattered filter, or Bayer filter, over the chip which passes specific wavelengths of light to each pixel. Thus, for example, a pixel that primarily detects red light will typically have two neighboring pixels that primarily detect green, and another neighboring pixel that detects blue. The specific four pixel pattern of two green, one red and one blue is generally repeated across the face of the image sensor. The camera contains a processor that interpolates the blue and green values it should use at a red pixel location from the respective color of neighboring pixels. This process of interpolation is repeated at each pixel to obtain the colors missing at each pixel. Such interpolation filtering is typically designed to produce visually appealing color rendering in images and may require 100 calculations at each pixel (depending on the algorithm and the desire to avoid visually distracting artifacts in the resulting images).
Some recent CMOS image sensor designs by Foveon bypass the Bayer filter approach and instead use a layered CMOS sensor in which the depth of photon penetration before detection is used to assign color. Blue is generally detected near the surface and red photons penetrate deepest before detection. These devices obviate the need for color interpolation, but are reported to have colors that are less well separated than possible with Bayer filters.
A pixel on a digital camera sensor collects photons which are converted into an electrical charge by its photodiode. Once the “bucket” is full, the charge caused by additional photons will overflow and have no effect on the pixel value, resulting in a clipped, overexposed, or saturated pixel value. Blooming occurs when this charge flows over to surrounding pixels, brightening or overexposing them in the process. Bloom can be a problem even in CCD devices that have anti-bloom circuitry. CMOS detectors have a big advantage over CCD sensors in that bloom is virtually nonexistent. Thus, saturation of a pixel has minimal effect on the neighboring pixels.
To extend dynamic range of an image or image-based system, the use of multiple varying exposures, or exposure bracketing, is known (see, for example, U.S. Pat. application Ser. No. 20030078737). In contrast to the present invention, others using these exposure-based techniques employ them in a simple manner qualitatively to enhance rendering contrast or in a manner of thresholding to quantify detected signal strength, and without employing knowledge of the imager filters' spectral response functions for added and quantifiable dynamic range.
Accordingly, what is needed is a non-invasive means of determining the functional and structural status of in situ optically dense samples such as tissues in a non-subjective manner using reflectance or transmission image-based spectroscopy. Further, such a means should also provide long term non-subjective assessment of the in situ sample in response to various conditions such as age, environment, trauma, as well as treatment modalities, including the therapeutic effectiveness of pharmaceuticals.
Moreover, methods and devices which exploit various types of attenuation factors and commensurate dynamic range gains for illumination bands in the visual to infrared light spectrum also need to be developed.
The present invention satisfies these needs, as well as others.